Abstract
The pervasiveness of GPS-enabled devices and wireless communication technologies flourish the market of Spatial Crowdsourcing (SC), which consists of location-based tasks and requires workers to physically be at specific locations to complete them. In this work, we study the problem of Worker Churn based Task Assignment in SC, where tasks are to be assigned by considering workers' churn. In particular, we aim to achieve the highest total rewards of task assignments based on the worker churn prediction. To solve the problem, we propose a two-phase framework, which consists of a worker churn prediction phase and a task assignment phase. In the first phase, we use an LSTM-based model to extract the latent feelings of workers based on the historical data and then estimate the idle time intervals of workers. In the assignment phase, we design an efficient greedy algorithm and a Kuhn-Munkras (KM)-based algorithm that can achieve the optimal task assignment. Extensive experiments offer insight into the effectiveness and efficiency of the proposed solutions.
Original language | English |
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Title of host publication | CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management |
Number of pages | 10 |
Publisher | Association for Computing Machinery |
Publication date | 26 Oct 2021 |
Pages | 2070-2079 |
ISBN (Electronic) | 9781450384469 |
DOIs | |
Publication status | Published - 26 Oct 2021 |
Event | 30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, Australia Duration: 1 Nov 2021 → 5 Nov 2021 |
Conference
Conference | 30th ACM International Conference on Information and Knowledge Management, CIKM 2021 |
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Country/Territory | Australia |
City | Virtual, Online |
Period | 01/11/2021 → 05/11/2021 |
Sponsor | ACM SIGIR, ACM SIGWEB |
Bibliographical note
Publisher Copyright:© 2021 ACM.
Keywords
- spatial crowdsourcing
- task assignment
- worker churn